Data Mining is probably one of the most important concept, It should be the beginning of any data mining technical training because, on one hand, it gives a very well-shaped idea about what data mining is and, on the other, it is not extremely technical, this work is only about frequent patterns and specifically, about frequent item sets, Frequent item-sets play an essential role in many data mining tasks that try to find interesting patterns from databases, such as association rules, correlations, sequences, episodes, classifiers, clusters and many more of which the mining of association rules is one of the most popular problems. The large amount of available customer data(like policy data, claim data, alteration data, underwriting data etc.) in the organizations and due to the rapid technical progress data recording is even more increased the trade-of for managing the data at one hand and analyzing the data at the other hand for exploring the hidden knowledge for business purposes.
The supply side of data management is characterized by huge data collection with a disordered structure, often mistaken, of doubtful quality and only partially integrated. On demand side we need abstract and high level information that is tailored to the user’s (mostly management people) needs and can be directly applied for improving the decision making processes.
For detecting new trends and elaborating suited strategies etc. in order to bridge gap between both sides, i.e., to find reasonable way for turning data into information, we need efficient algorithms that can perform parts of necessary transformation automatically, there will always remain interactive steps for this data analysis and information gathering task.
As nowadays there are many communication companies in market, to gain competitive advantage, companies have to analyze their produced data in an intelligent manner.
To fulfill the above requirement Data analysis should be used in advanced manner by using data mining and statistical techniques to analyze their data by generating multidimensional analysis reports, graphs etc
Company have to Predict the future patterns of their claim data and take appropriate decisions to take advantage of available resources .In This Research we have to analyze the SLIC claim data. Finding and predicting the future patterns of SLIC claim data for analysis.
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